high frequency data

In this research, we use jump beta and continuous beta as indicators of financial sector companies systematic risk and study their determinants in banking, insurance and investment industry. In result, the value of jump beta is higher than continuous beta. Jump beta of Banking industry and Investment industry is considerably lower than average. We found some negative and positive effects of firm characteristics on jump beta and continuous beta. In insurance companies, the supremacy of jump beta is influenced by firm characteristics. Size has positive effect on aggressiveness of both continuous and jump betas in investment companies. Current ratio has positive effect and debt ratio has negative effect on aggressiveness of insurance companies. Firm characteristic has some positive and negative effects on continuous industry beta deviation, but no effect on jumpy one. Inflation has negative effect on continuous beta but has no considerable effect on jump beta. Inversely, exchange rate has negative effect on jump beta but has no sensible effect on continuous beta. Influence of growth rate is strong positive for all industries of financial sector but weak positive for banking and insurance companies

Noise is essential for the existence of a liquid market, and if noise traders are not present in the market, the trade volume will drop severely and an important aspect of the market philosophy will be lost. However, these noise traders bring noise to the market, and the existence of noise in prices indicates a temporary deviation in prices from their fundamental values. In particular, high-frequency prices carry a significant amount of noise that is not eliminated by averaging. If the level of noise in stock prices remains high for a period of time, it can be identified as a risk factor because it indicates that the deviation from fundamental values has been sustained. In this paper, after estimating the microstructure noise in high-frequency prices through a modified parametric approach, using a portfolio switching method, we compared the performance of portfolios having a high level of noise with the performance of portfolios having a lower level of noise and concluded that the risk of the high noise level presents itself as a risk premium in the future return and that asset pricing models which capture the systematic risks cannot capture the noise risk in prices. Keywords: Microstructure noise; High frequency data; Quasi-maximum Likelihood Estimation (QMLE); Portfolio switching. JEL Classification: C13, G11, G12